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Possible contribution of quantum-like correlations to the placebo effect: consequences on blind trials

June 2017
Theoretical Biology and Medical Modelling 14(1)

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Background. Factors that participate in the biological changes associated with a placebo are not completely understood. Natural evolution, mean regression, concomitant procedures and other non specific effects are well-known factors that contribute to the “placebo effect”. In this article, we suggest that quantum-like correlations predicted by a probabilistic modeling could also play a role. Results. An elementary experiment in biology or medicine comparing the biological changes associated with two placebos is modeled. The originality of this modeling is that experimenters, biological system and their interactions are described together from the standpoint of a participant who is uninvolved in the measurement process. Moreover, the small random probability fluctuations of a “real” experiment are also taken into account. If both placebos are inert (with only different labels), common sense suggests that the biological changes associated with the two placebos should be comparable. However, the consequence of this modeling is the possibility for two placebos to be associated with different outcomes due to the emergence of quantum-like correlations. Conclusion. The association of two placebos with different outcomes is counterintuitive and this modeling could give a framework for some unexplained observations where mere placebos are compared (in some alternative medicines for example). This hypothesis can be tested in blind trials by comparing local vs. remote assessment of correlations.

Description of an experiment from an uninvolved standpoint. The observer O measures the system S whereas the participant P remains uninvolved in the measurement (he does not interact with O and S). P knows that O has observed a definite state of S, but he does not know which one. If P finally interacts with O and S, then P and O agree on the outcome of S. The reasoning can be continued with another participant Q who does not interact with S, O and P. What Q can say is that S, O and P have definite values that are correlated. The only thing that an uninvolved participant can do is to describe the form, but not the content of the information available to the observers who interact with S and with each other. Thus, the consistency of any measurement is guaranteed (GNU Free Documentation License)

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Probabilistic space constructed by an uninvolved participant P to predict the outcomes of the experiments. A team of interacting experimenters O and O' is described from the standpoint of an uninvolved participant who knows the initial experimental conditions (Fig. 2). We suppose a probability equal to p for the event " direct relationship " and equal to q for the event " reverse relationship " (p + q = 1). Each observer has his own probabilistic expectations and P assigns the probability p to O as the best estimate that O can make for the future observation of a direct relationship; the same probability is assigned to O' independently of O since the probabilistic expectations are specific to each observer. White areas are unauthorized experimental situations with incompatible outcomes after interaction of O and O' (e.g. " direct " for O and " reverse " for O'). Therefore, the probability that the experimenters observe a direct relationship is calculated by dividing the central gray area ( " direct " for both observers) by the sum of the probabilities of possible outcomes (either " direct " or " reverse " for both observers), namely all gray areas. Ω, probability space

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Calculation of the probability of a direct relationship. The evolution of the probability that a team (composed of two members who interact) observes a direct relationship is described in this figure by taking into account successive probability fluctuations. The probability defined in Fig. 3 is calculated according to the mathematical sequence in the cartouche. Each successive probability p n+1 of the sequence is calculated by using p n and a random probability fluctuation is randomly obtained between −0.5 and +0.5 × 10-15. This computer simulation shows that the initial state with a probability of 1/2 is in fact metastable and, after a dramatic transition, one of two stable positions is achieved: either Prob (direct) = 1 or Prob (direct) = 0. With N > 2 or with higher values of probability fluctuations, a transition is obtained after a lower number of calculation steps (data not shown). Eight computer simulations are reported in this figure

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Comparison of local vs. remote assessment in an experiment with two placebos. In an experiment with a local assessment (local blind design or open-label experiment), correlations between labels (Pcb 0 and Pcb 1 ) and states of the biological system (↓ and ↑) emerge (b and e) from the initial state (a and d). These correlations vanish if the assessment of the experiment is made in a blind experiment with a remote supervisor (c and f). In this latter case, the difference between the biological changes associated with Pcb 0 and Pcb 1 is not statistically significant (NS) and the biological changes ( " ↑ " ) are randomly distributed among the two placebos. The difference of results in local vs. remote assessments offers the opportunity to test the modeling

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From a classical description of the experimental situation to the present modeling. The experimental situation depicted in Fig. 3 is generalized in this figure by using the parameter d which varies with the degree of independence of the probabilistic expectations on the outcome assigned to O and O'. The values of the two areas with impossible situations (direct relationship for one observer and reverse relationship for the other one) are calculated as: p-(p 2 + d) = p × (1-p)-d = pq-d. For d = 0, correlations between labels and biological outcomes emerge and, for d = pq, the probability of a direct relationship is equal to p as in classical probability. Ω, probability space

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Five challenges for manual therapies trials with placebo controls: A proposal

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Randomised placebo-controlled trials are implemented to determine whether a particular therapy is superior to placebo and can thus be considered, effective. However, adopting the standard RCT design in contexts other than pharmacological trials, such as manual therapy, may result in systematic biases. These biases may occur due to: the impossibility of traditional “double-blinding” in manual therapy trials; insufficient pre-training of operators delivering the treatment and/or sham therapy; biased recruitment of study participants; the problematic use of subjective and/or objective outcomes; and finally, the presence of phenomena mimicking placebo effects. From the perspective of placebo studies, the purpose of this paper is to discuss and make appropriate recommendations to address these five issues in manual therapy research.

Differences between experimental and placebo arms in manual therapy trials: a methodological review

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Aug 2022
BMC MED RES METHODOL

Background To measure the specific effectiveness of a given treatment in a randomised controlled trial, the intervention and control groups have to be similar in all factors not distinctive to the experimental treatment. The similarity of these non-specific factors can be defined as an equality assumption. The purpose of this review was to evaluate the equality assumptions in manual therapy trials. Methods Relevant studies were identified through the following databases: EMBASE, MEDLINE, SCOPUS, WEB OF SCIENCE, Scholar Google, clinicaltrial.gov, the Cochrane Library, chiloras/MANTIS, PubMed Europe, Allied and Complementary Medicine (AMED), Physiotherapy Evidence Database (PEDro) and Sciencedirect. Studies investigating the effect of any manual intervention compared to at least one type of manual control were included. Data extraction and qualitative assessment were carried out independently by four reviewers, and the summary of results was reported following the PRISMA statement. Result Out of 108,903 retrieved studies, 311, enrolling a total of 17,308 patients, were included and divided into eight manual therapy trials categories. Equality assumption elements were grouped in three macro areas: patient-related, context-related and practitioner-related items. Results showed good quality in the reporting of context-related equality assumption items, potentially because largely included in pre-existent guidelines. There was a general lack of attention to the patient- and practitioner-related equality assumption items. Conclusion Our results showed that the similarity between experimental and sham interventions is limited, affecting, therefore, the strength of the evidence. Based on the results, methodological aspects for planning future trials were discussed and recommendations to control for equality assumption were provided.

Benveniste’s Experiments Explained by a Non-Conventional Experimenter Effect

Preprint

Full-text available

Mar 2018

Francis Beauvais

Background: Benveniste’s biology experiments suggested the existence of molecular-like effects without molecules (“memory of water”). In this article, it is proposed that these disputed experiments could have been the consequence of a previously unnoticed and non-conventional experimenter effect. Methods: A probabilistic modelling is built in order to describe an elementary laboratory experiment. A biological system is modelled with two possible states (“resting” and “activated”) and exposed to two experimental conditions labelled “control” and “test”, but both biologically inactive. The modelling takes into account not only the biological system, but also the experimenters. In addition, an outsider standpoint is adopted to describe the experimental situation. Results: A classical approach suggests that, after experiment completion, the “control” and “test” labels of biologically-inactive conditions should be both associated with “resting” state (i.e. no significant relationship between labels and system states). However, if the fluctuations of the biological system are also considered, a quantum-like relationship emerges and connects labels and system states (analogous to a biological “effect” without molecules). Conclusions: No hypotheses about water properties or other exotic explanations are needed to describe Benveniste’s experiments, including their unusual features. This modelling could be extended to other experimental situations in biology, medicine and psychology.

“Memory of Water” Experiments Explained with No Role Assigned to Water: Pattern Expectation after Classical Conditioning of the Experimenter

Preprint

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Feb 2020

Francis Beauvais

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Benveniste’s Experiments Explained by a Non-Conventional Experimenter Effect

Article

Full-text available

Mar 2018

Francis Beauvais

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Are biological systems and experimenters really separated?

Preprint

Full-text available

Jan 2018

Francis Beauvais

Background. In experimental sciences, conception of an experiment and record of the outcomes must be strictly separated. Although many possible pitfalls have been described, particularly in biological sciences, one cannot exclude unknown loopholes. Methods. A simple probabilistic modeling is constructed in order to describe experimenters testing the hypothesis of a relationship between some experimental conditions (supposed causes) and states of a biological system (observed effects). The modeling rests on two preliminary remarks. First, after assessment of a relationship, the outcome is not a property of the system alone, but is a property of the experimenters and the system taken as a whole. Second, as a consequence, the outcome does not preexist to measurement. Results. A biological system with two possible states ("resting" and "activated") exposed to two control conditions distinguished only by their "labels" is modeled. A classical approach suggests that the two control conditions are both associated with the "resting" state (i.e. no relationship). Nevertheless, if the fluctuations of the system are considered, the hypothesis of a significant relationship between "labels" and system states is confirmed. In contrast, if the outcomes are not globally recognized as a relationship, but remains unconnected by the experimenters, no significant relationship emerges. Conclusion. This probabilistic modeling suggests that, despite precautions, the strict separation of biological systems and experimenters is an ideal not necessarily achieved when the hypothesis of a relationship is tested. The consequences could be wrong conclusions about causal relationships. Specific blind procedures are proposed to prevent unwanted correlations involving the experimenters.

Are biological systems and experimenters really separated?

Preprint

Jan 2018

Francis Beauvais

Background. In experimental sciences, conception of an experiment and record of the outcomes must be strictly separated. Although many possible pitfalls have been described, particularly in biological sciences, one cannot exclude unknown loopholes. Methods. A simple probabilistic modeling is constructed in order to describe experimenters testing the hypothesis of a relationship between some experimental conditions (supposed causes) and states of a biological system (observed effects). The modeling rests on two preliminary remarks. First, after assessment of a relationship, the outcome is not a property of the system alone, but is a property of the experimenters and the system taken as a whole. Second, as a consequence, the outcome does not preexist to measurement. Results. A biological system with two possible states (“resting” and “activated”) exposed to two control conditions distinguished only by their “labels” is modeled. A classical approach suggests that the two control conditions are both associated with the “resting” state (i.e. no relationship). Nevertheless, if the fluctuations of the system are considered, the hypothesis of a significant relationship between “labels” and system states is confirmed. In contrast, if the outcomes are not globally recognized as a relationship, but remains unconnected by the experimenters, no significant relationship emerges. Conclusion. This probabilistic modeling suggests that, despite precautions, the strict separation of biological systems and experimenters is an ideal not necessarily achieved when the hypothesis of a relationship is tested. The consequences could be wrong conclusions about causal relationships. Specific blind procedures are proposed to prevent unwanted correlations involving the experimenters.

“Memory of water” experiments explained with no role assigned to water: Pattern expectation after classical conditioning of the experimenter

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Purpose of review: Recent articles have summarized the literature on the neurobiological mechanisms involved in placebo effects. In this article, we review and evaluate the status of the psychological mechanisms in theory and research regarding placebo effects. Recent findings: Currently, the placebo effect literature concentrates more on neurobiological mechanisms than on psychological mechanisms. Both theoretical and empirical coverage of the psychological mechanisms are typically limited to two variables: conditioning and verbally induced expectations. Because psychological processes take center stage in mediating the link between the therapeutic context and placebo responding, greater effort is needed to build empirically derived and theoretically complex psychological process models. Such models would include a broader array of psychological constructs and mechanisms. Summary: Research and theory on placebo effects has illuminated much regarding the neurobiological mechanisms. The psychological mechanisms, however, have received much less attention. Expanding our knowledge regarding the psychological processes involved in placebo responding would open up opportunities for developing nondeceptive intervention techniques that encourage placebo responses. Ultimately, a concerted empirical effort to clarify the psychological model underlying placebo effects could merge with the evolving neurobiological model to fulfill the promise that placebo effects have for improving patient outcomes.

First evidence of Beauvais' hypothesis in a plant model

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Introduction: Beauvais presented the application of a so-called 'quantum-like model of homeopathy' by introducing the idea of a type of randomization/unblinding which he called 'in situ'. He predicted that randomized studies based on this type of randomization/unblinding lead to more pronounced effects in placebo controlled randomized homeopathic trials. We designed an experiment regarding wheat germination and stalk length to investigate Beauvais' idea of 'in situ randomization/unblinding' using a homeopathic dilution of sulphur (LM VI) as compared to placebo as well as to water. Aim and method: The primary aim of this double-blind randomized controlled experiment was to investigate whether there are differences of 'in situ randomization/unblinding' vs 'central randomization/unblinding' with respect to the effect of a homeopathic substance compared to placebo. The secondary aim of our study was to examine possible differences between the sulphur and the placebo group in the 'in situ' arm regarding germination and/or stalk growth of wheat seedlings measured after a seven days exposure. Wheat was treated either with sulphur LM VI, placebo, or water. The wheat grains were placed on glass lids and treatment was performed following the 'in situ randomization/unblinding' as well as 'central randomization/unblinding' method. Germination was measured and classified into three categories. Results: Under 'in situ' randomization/unblinding the odds of a seed not to germinate is 40% lower if treated with sulphur compared to placebo (p=0.004). In contrast, these odds are practically equal in the 'central' meta-group (OR=1.01, p=0.954). Under 'in situ' randomization/unblinding the odds of a seed to germinate with a length ≥1mm is practically equal if treated with sulphur or with placebo (OR=0.96, p=0.717). In contrast, these odds are 21% higher under sulphur compared to placebo in the 'central' meta-group (OR=1.21, p=0.062). In summary, we found a sulphur effect that is significantly different between 'in situ' and 'central' randomization/unblinding relating to all three stages of germination.

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